Meeting Title: MatterMore x Brainforge | Standup Date: 2025-06-09 Meeting participants: Mathew’s Notetaker (Otter.ai), Trevor’s Notetaker (Otter.ai), Fireflies.ai Notetaker Awaish, Amber Lin, Luke Daque, Annie Yu
WEBVTT
1 00:00:39.730 ⇒ 00:00:41.810 Amber Lin: They’ve added a long
2 00:00:46.180 ⇒ 00:00:47.055 Amber Lin: value
3 00:00:53.180 ⇒ 00:00:54.270 Amber Lin: game, boy.
4 00:00:55.520 ⇒ 00:00:56.590 Amber Lin: We got it.
5 00:01:25.300 ⇒ 00:01:26.990 Amber Lin: Hi!
6 00:01:28.680 ⇒ 00:01:29.999 Luke Daque: Hi! Amber! How’s it going.
7 00:01:30.445 ⇒ 00:01:49.100 Amber Lin: Pretty good. I I thought. I’ll be back in La by now, but it turns yesterday before I went to the airport I was like, why don’t I have the check in email? And my friends are helping me, Logan. It turns out I booked it for today and not yesterday.
8 00:01:49.100 ⇒ 00:01:49.925 Luke Daque: Oh!
9 00:01:50.750 ⇒ 00:01:57.330 Amber Lin: So I had to find a place to stay. And now I’m going back flying back later today.
10 00:01:57.760 ⇒ 00:01:59.200 Luke Daque: And it’s sucks.
11 00:01:59.755 ⇒ 00:02:00.310 Amber Lin: Yeah.
12 00:02:00.310 ⇒ 00:02:00.990 Luke Daque: Yeah.
13 00:02:01.510 ⇒ 00:02:04.339 Amber Lin: Good morning, Annie!
14 00:02:04.340 ⇒ 00:02:05.850 Annie Yu: Hello, Amber! Hello, Luke.
15 00:02:06.810 ⇒ 00:02:07.530 Luke Daque: Hello! Everyone.
16 00:02:14.880 ⇒ 00:02:16.839 Amber Lin: On a holiday, I think.
17 00:02:16.840 ⇒ 00:02:18.810 Amber Lin: Oh, what? Oh.
18 00:02:18.810 ⇒ 00:02:20.920 Annie Yu: That’s their their holiday.
19 00:02:21.216 ⇒ 00:02:26.549 Amber Lin: Damn it, I don’t know that. Okay, so we gotta figure out this on our own. It seems.
20 00:02:29.740 ⇒ 00:02:32.010 Amber Lin: Let me open.
21 00:02:32.540 ⇒ 00:02:34.480 Luke Daque: Is this an internal meeting, or are we.
22 00:02:34.480 ⇒ 00:02:35.500 Amber Lin: Internal meeting. I?
23 00:02:35.500 ⇒ 00:02:38.222 Amber Lin: Oh, okay, they’re no takers.
24 00:02:38.770 ⇒ 00:02:39.720 Luke Daque: Okay. Cool. Cool.
25 00:02:39.720 ⇒ 00:02:40.500 Amber Lin: Shit.
26 00:02:41.140 ⇒ 00:02:47.089 Amber Lin: Yeah, I just wanted to use that link. So I looked at the linear. And I guess the
27 00:02:47.510 ⇒ 00:02:58.270 Amber Lin: the thing that we’re not that clear about share my screen.
28 00:03:04.460 ⇒ 00:03:05.065 Amber Lin: So
29 00:03:07.932 ⇒ 00:03:17.189 Amber Lin: gonna hopefully, set some power bi. And Annie, I know you’re just blocked until he does that. So that’s fine.
30 00:03:17.360 ⇒ 00:03:22.690 Amber Lin: We? I think this one Trevor, was one. I think this is probably in progress.
31 00:03:23.300 ⇒ 00:03:25.210 Amber Lin: Right, Luke. I I saw.
32 00:03:25.210 ⇒ 00:03:25.660 Luke Daque: Yes.
33 00:03:25.660 ⇒ 00:03:27.510 Amber Lin: Trevor’s response.
34 00:03:27.510 ⇒ 00:03:31.619 Luke Daque: Yeah, I’ll be working on that one, he just re- responded with the yesterday.
35 00:03:32.610 ⇒ 00:03:40.739 Amber Lin: Okay, okay. So I think that by the end of today would be nice. And then.
36 00:03:42.060 ⇒ 00:03:46.520 Amber Lin: like, mostly, I, I wanted a way to figure out this.
37 00:03:48.200 ⇒ 00:03:50.790 Luke Daque: Like. I want his help to figure out how to do.
38 00:03:50.790 ⇒ 00:03:57.140 Luke Daque: I think I already created that in our Google sheet. That’s what I worked on last.
39 00:03:57.140 ⇒ 00:04:02.340 Luke Daque: Oh, try so wait! Let me let me send it. Oh, maybe it’s in the.
40 00:04:02.990 ⇒ 00:04:06.939 Luke Daque: It’s in the external matter. More channel. Actually.
41 00:04:07.070 ⇒ 00:04:07.790 Amber Lin: Oh,
42 00:04:09.510 ⇒ 00:04:19.609 Amber Lin: I mean, my question is that cause I I don’t really know how technically this is gonna be done. So we have the dimensions right? We have it mapped out.
43 00:04:20.370 ⇒ 00:04:28.439 Amber Lin: Does that cover all of those? The key dimensions that they shared?
44 00:04:28.670 ⇒ 00:04:34.200 Amber Lin: Go, say, does it cover everything here. I don’t like
45 00:04:34.370 ⇒ 00:04:39.389 Amber Lin: time grades, primary segment, secondary segment filters. Does it cover everything.
46 00:04:41.810 ⇒ 00:04:45.980 Luke Daque: I think so. Yeah, cause I, I created it based on the current
47 00:04:46.910 ⇒ 00:04:50.276 Luke Daque: views that we have basically the the ones that
48 00:04:50.790 ⇒ 00:04:55.170 Luke Daque: and the ones that the metrics that Annie added in the Google sheet.
49 00:04:57.020 ⇒ 00:05:02.490 Luke Daque: But yeah, we’ll we’ll have to break those down to all the time. Greens that they have here all the.
50 00:05:02.880 ⇒ 00:05:04.440 Amber Lin: Yeah. That was, that was.
51 00:05:04.440 ⇒ 00:05:04.800 Luke Daque: For me.
52 00:05:04.800 ⇒ 00:05:18.019 Amber Lin: Why I kind of wanted a wish is because we we won. Well, it will be really helpful when we transferred everything into Dbt. Today the task that you’re working on. And then I guess tomorrow.
53 00:05:18.240 ⇒ 00:05:22.610 Amber Lin: when a wish is online, I want us to work with him to figure out
54 00:05:22.890 ⇒ 00:05:32.970 Amber Lin: how we’re gonna enable this like this granularity, and especially how we’re gonna enable Annie to
55 00:05:33.230 ⇒ 00:05:42.390 Amber Lin: like use these as filters. So, Anna, you’re our internal client to figure out how these would work best for you.
56 00:05:42.850 ⇒ 00:05:45.620 Amber Lin: And also, I think another task is to
57 00:05:45.940 ⇒ 00:05:50.060 Amber Lin: transfer all the pipe like the current python stuff.
58 00:05:50.740 ⇒ 00:05:59.340 Amber Lin: And like, do you guys, we think we should transfer the internal python stuff into the Dbt, or should we just create new ones
59 00:05:59.540 ⇒ 00:06:03.440 Amber Lin: from scratch like based on these.
60 00:06:05.991 ⇒ 00:06:08.500 Annie Yu: I’m not following that question.
61 00:06:08.500 ⇒ 00:06:23.690 Amber Lin: Okay, that’s okay. So right now, we have. Trans, we have transformations, right? Things that you did some manipulation. Some of them are in the bigquery views that Luke did. Some of them you did directly. Hard coded into Python right.
62 00:06:24.770 ⇒ 00:06:32.199 Annie Yu: And I would assume that based on Luke’s work last week. They are already in the document, so
63 00:06:32.580 ⇒ 00:06:34.670 Annie Yu: at least the measure, the.
64 00:06:34.670 ⇒ 00:06:36.270 Amber Lin: Oh, yeah. Okay, so then.
65 00:06:36.270 ⇒ 00:06:36.730 Annie Yu: Okay.
66 00:06:36.730 ⇒ 00:06:43.700 Amber Lin: Okay, sounds good. And for those measures, do we have a tab that indicates if it’s in, say.
67 00:06:44.202 ⇒ 00:06:54.069 Amber Lin: where it is like, is it hard coded in Python? Or is it like somewhere in Dbt or somewhere in bigquery? Do we have something that distinguishes that.
68 00:06:55.867 ⇒ 00:07:04.929 Annie Yu: I’m not sure. But also, I think my point is, if it’s in Python Luke will have to write different for using anyway. So.
69 00:07:04.930 ⇒ 00:07:06.410 Amber Lin: Yeah, yeah, true.
70 00:07:06.835 ⇒ 00:07:13.784 Amber Lin: I, yeah, I think we’re talking about the same thing. I essentially was just like, Oh, does Luke need to write that.
71 00:07:14.740 ⇒ 00:07:15.940 Amber Lin: Okay, okay.
72 00:07:16.520 ⇒ 00:07:33.630 Amber Lin: okay. So, Luke, a lot of work is for you, very unfortunately, because last few times Annie has been very busy. So busy with the work from Adam or so I think this week you’ll have a lot heavier tasks from Adam or okay. So let me.
73 00:07:33.630 ⇒ 00:07:40.500 Annie Yu: One more thing I’m not sure if it’s noted here or in in Luke’s note, is the the local time.
74 00:07:40.810 ⇒ 00:07:45.600 Annie Yu: just I think we just have to make sure that all the time is based on local time.
75 00:07:47.000 ⇒ 00:07:47.950 Luke Daque: Right?
76 00:07:49.590 ⇒ 00:07:53.389 Luke Daque: Yeah, we’ll have to probably add a time zone field.
77 00:07:53.740 ⇒ 00:07:55.770 Luke Daque: So we’ll know. Like, what.
78 00:07:56.140 ⇒ 00:07:59.739 Luke Daque: But should I think we have to have like 2
79 00:08:00.270 ⇒ 00:08:04.459 Luke Daque: time zones like one that’s in local time, and maybe one that’s like.
80 00:08:05.100 ⇒ 00:08:13.790 Luke Daque: I don’t know, like GMT or whatever. So that that way we can easily does it?
81 00:08:15.140 ⇒ 00:08:20.859 Luke Daque: Compare time zones, or like time, time, dimensions, right?
82 00:08:21.900 ⇒ 00:08:23.239 Luke Daque: Or what do what do you think.
83 00:08:23.921 ⇒ 00:08:27.009 Annie Yu: Sure I don’t. I just I don’t.
84 00:08:27.612 ⇒ 00:08:31.000 Annie Yu: I can’t think of a use case where I would need all
85 00:08:31.220 ⇒ 00:08:38.200 Annie Yu: all things in the same time zone as of now. But if you think it’s helpful to include one field, I don’t think that’s
86 00:08:38.370 ⇒ 00:08:43.580 Annie Yu: gonna do any harm but but when we talk about time grants
87 00:08:44.020 ⇒ 00:08:49.330 Annie Yu: like day of week time of day. I think those things have to be based on the local time.
88 00:08:49.870 ⇒ 00:08:52.250 Luke Daque: Okay. Yeah. Sounds good.
89 00:08:56.330 ⇒ 00:08:56.850 Amber Lin: Okay.
90 00:08:57.803 ⇒ 00:09:13.820 Amber Lin: I mean, look, you can work on you’ll you’ll have it. You have a ticket today. And I think this one we really need to list out all the things that we’re gonna do. I think we already have some requirements here. But we should identify. Say.
91 00:09:14.460 ⇒ 00:09:17.950 Amber Lin: what is it exactly that you need to do for the modeling.
92 00:09:19.240 ⇒ 00:09:23.740 Amber Lin: I imagine that’s not the clearest for you yet. Right?
93 00:09:24.780 ⇒ 00:09:26.630 Amber Lin: Right.
94 00:09:26.630 ⇒ 00:09:30.990 Annie Yu: Yeah, I’m sure Luke has to do more than this just because these are metrics. But then there are.
95 00:09:30.990 ⇒ 00:09:31.700 Amber Lin: Hmm.
96 00:09:31.700 ⇒ 00:09:32.800 Annie Yu: Dimension.
97 00:09:33.320 ⇒ 00:09:41.209 Amber Lin: I see I’m a teeny bit confused, but that’s okay. That’s exactly what
98 00:09:41.701 ⇒ 00:09:52.108 Amber Lin: we’ll we’ll do. So it seems like, since the wage is not here. I didn’t know that was gonna happen. We probably should book a meeting for tomorrow.
99 00:09:52.620 ⇒ 00:09:58.470 Luke Daque: Yeah, that’d be fine, because, like today, most of my time will be setting up the it’s not.
100 00:09:58.470 ⇒ 00:09:58.930 Amber Lin: Okay.
101 00:09:58.930 ⇒ 00:10:01.759 Luke Daque: I’ll not. I’ll probably not be able to start working on.
102 00:10:01.760 ⇒ 00:10:02.310 Amber Lin: Yeah.
103 00:10:02.310 ⇒ 00:10:03.179 Luke Daque: Models today.
104 00:10:03.180 ⇒ 00:10:03.800 Amber Lin: I imagine.
105 00:10:03.800 ⇒ 00:10:04.180 Luke Daque: So.
106 00:10:04.180 ⇒ 00:10:10.110 Amber Lin: So great. Let me ping a wish. Let me grab a time, and they’ll book a meeting for us tomorrow.
107 00:10:10.110 ⇒ 00:10:10.900 Luke Daque: Oh, that’s good!
108 00:10:10.900 ⇒ 00:10:11.425 Amber Lin: Awesome.
109 00:10:11.950 ⇒ 00:10:12.350 Annie Yu: Thank you.
110 00:10:12.350 ⇒ 00:10:12.790 Amber Lin: Everyone.
111 00:10:12.790 ⇒ 00:10:17.737 Annie Yu: I do have a question. So for the power bi this thing
112 00:10:18.930 ⇒ 00:10:41.010 Annie Yu: and my question is just for the next step, everything in power bi like I should just anchor on that document that better, more shared. Right? So if there’s something like that’s not in the document. And we built over the past few weeks. We don’t have to worry about that part, right? We just focus on the document document.
113 00:10:41.430 ⇒ 00:10:43.700 Amber Lin: Yeah. Which document are you talking about?
114 00:10:44.590 ⇒ 00:10:47.769 Amber Lin: You mean the the sorry, this one that they shared.
115 00:10:47.940 ⇒ 00:10:52.820 Annie Yu: The is it this one? Yeah, like the this?
116 00:10:54.130 ⇒ 00:10:56.469 Annie Yu: Yeah, I think that’s it. That’s just.
117 00:10:56.470 ⇒ 00:10:57.090 Amber Lin: Okay.
118 00:10:57.090 ⇒ 00:10:59.710 Annie Yu: Copy from their document.
119 00:10:59.990 ⇒ 00:11:20.220 Amber Lin: Okay, I think that’s something that cause essentially what they want is not really the visualizations. They want, the ability to do these filters. Right. So I think a big part of it is Luke’s work, and when we confirm what we need to do for the modeling for them, we can also confirm, like what they exactly want to see on power bi.
120 00:11:20.400 ⇒ 00:11:24.520 Amber Lin: But let me ping Trevor on the power bi instance.
121 00:11:25.444 ⇒ 00:11:34.299 Annie Yu: And one more thing about power bi, just so you know, power bi like tableau. There’s also like power bi decks, desktop and power
122 00:11:34.940 ⇒ 00:11:55.549 Annie Yu: web version. But the thing about power Bi is it’s not supported on Mac OS. That means like it on Mac. There’s not like a direct link to download a a desktop. I I believe, with Mac OS. You can still access the web version, but just not the desktop, and I’m not sure how
123 00:11:56.105 ⇒ 00:12:14.350 Annie Yu: in terms of the features, how different they are. But that’s just one thing like I I believe I will be the web version, and I know that there are some workarounds where macbook users can also have power bi desktop, but that involves like.
124 00:12:14.510 ⇒ 00:12:16.690 Amber Lin: You need install. Window instance.
125 00:12:17.440 ⇒ 00:12:18.190 Annie Yu: Desktop
126 00:12:18.750 ⇒ 00:12:36.789 Annie Yu: like I’m not comfortable like downloading a local virtual desktop, but there’s also, like cloud hosted virtual desktop that could be an option. But I don’t like. I doubt that that’s something they’ll they’ll want to go with. But I think just just so, everyone know, like, if everyone’s okay with me using to spur version.
127 00:12:37.880 ⇒ 00:12:38.610 Amber Lin: And I have my.
128 00:12:38.610 ⇒ 00:12:44.400 Amber Lin: I need to check how different it is. I don’t know how much we can do when it’s like
129 00:12:44.720 ⇒ 00:12:45.460 Amber Lin: online.
130 00:12:45.460 ⇒ 00:12:46.100 Luke Daque: Yeah.
131 00:12:46.100 ⇒ 00:12:46.820 Annie Yu: Yeah.
132 00:12:46.820 ⇒ 00:12:49.370 Luke Daque: I don’t. I think it’s very limited from.
133 00:12:49.890 ⇒ 00:12:50.640 Amber Lin: From where?
134 00:12:50.640 ⇒ 00:12:55.169 Amber Lin: Oh, yeah, yeah, where you share it? Yeah.
135 00:12:55.170 ⇒ 00:12:57.390 Annie Yu: Not yet. Yeah.
136 00:12:58.130 ⇒ 00:13:05.280 Amber Lin: I think that is where that you raise this. Let’s discuss it like, maybe with internally, we’ll figure out how to.
137 00:13:06.050 ⇒ 00:13:08.039 Amber Lin: We should figure out how to do that.
138 00:13:08.040 ⇒ 00:13:19.370 Annie Yu: Yeah, I I just know that it’s doable with the virtual desktop. But in terms of text, the virtual desktops there are like cloud hosted, and there’s also, like local
139 00:13:19.840 ⇒ 00:13:35.900 Annie Yu: installed, which, like the latter, wouldn’t work for me because I don’t want to slow down on my machine. But the cloud hosted like azure or aws, should work. But that’s something that should be managed by the client. I believe not. Not us.
140 00:13:36.780 ⇒ 00:13:39.839 Amber Lin: Oh, you mean the sorry. What should be managed by the client?
141 00:13:40.495 ⇒ 00:13:47.050 Amber Lin: Like. You see, there’s like the the 1st install and run power bi on a cloud virtual machine. The.
142 00:13:47.230 ⇒ 00:13:50.060 Annie Yu: Yeah, that’s something that will work.
143 00:13:51.850 ⇒ 00:13:54.880 Annie Yu: But then, to figure that out, I don’t think now
144 00:13:55.570 ⇒ 00:13:57.719 Annie Yu: something that should manage by us. If.
145 00:13:57.720 ⇒ 00:14:02.300 Amber Lin: Yeah. Cause if we’re gonna control their PC, then they should set up that.
146 00:14:02.300 ⇒ 00:14:12.379 Annie Yu: Yeah, I also do have like a older windows laptop at home. But I also like I’m not confident like how how fast.
147 00:14:12.737 ⇒ 00:14:31.699 Amber Lin: Do you want to test that? Maybe like, if today, you can see if you can download power bi desktop and just see if it even downloads at the reasonable speed I don’t raise that issue to, because I think that’s not something we should deal with, but any you should also just.
148 00:14:32.090 ⇒ 00:14:37.020 Annie Yu: Is that something we can download and use for free that I don’t know.
149 00:14:37.020 ⇒ 00:14:43.960 Luke Daque: Yeah, I don’t think so. You need you need an account like a Microsoft. 3, 6, 5 account, something like that. Yeah.
150 00:14:43.960 ⇒ 00:14:44.750 Amber Lin: Oh, I.
151 00:14:44.750 ⇒ 00:14:47.260 Luke Daque: So they don’t use it. So yeah, it sucks.
152 00:14:47.840 ⇒ 00:14:48.375 Amber Lin: Oh.
153 00:14:49.200 ⇒ 00:14:57.979 Amber Lin: I think we can download it. You can’t use it, but you can download it. I just I think I just mostly wanted to you to see if your computer is fast enough.
154 00:15:01.000 ⇒ 00:15:04.395 Annie Yu: Yeah, yeah, that could be an. But also
155 00:15:04.980 ⇒ 00:15:07.060 Annie Yu: like, I will be work from
156 00:15:09.930 ⇒ 00:15:13.249 Annie Yu: different countries in the next couple of weeks.
157 00:15:13.680 ⇒ 00:15:16.690 Annie Yu: So I don’t wanna bring 2 laptops in that sense.
158 00:15:16.950 ⇒ 00:15:17.470 Amber Lin: Hmm.
159 00:15:17.470 ⇒ 00:15:18.530 Luke Daque: Yeah.
160 00:15:18.530 ⇒ 00:15:23.389 Amber Lin: Okay, okay, so let’s raise the issue to. We’ll figure that out.
161 00:15:23.750 ⇒ 00:15:24.300 Luke Daque: Okay.
162 00:15:24.300 ⇒ 00:15:29.320 Annie Yu: Yeah, but I I did some quick research on the web version versus
163 00:15:29.830 ⇒ 00:15:42.220 Annie Yu: best top. I I mean, like, if we’re not like doing like heavy lifting in power. Bi, I think just building bar charts and all that should be fine. But I could also be wrong.
164 00:15:44.410 ⇒ 00:15:49.039 Amber Lin: Well that I think the web version is a lot more limited. Okay, let’s
165 00:16:03.330 ⇒ 00:16:08.320 Amber Lin: sounds good. I’m gonna type this issue in. And then hopefully, he has a response.
166 00:16:14.170 ⇒ 00:16:15.590 Annie Yu: Sounds good.
167 00:16:18.330 ⇒ 00:16:18.850 Amber Lin: Hmm.
168 00:16:21.530 ⇒ 00:16:22.140 Luke Daque: Cool.
169 00:16:23.430 ⇒ 00:16:29.972 Annie Yu: Look, you’re you’re familiar with power. Bi. Is that it? Cause? I feel like you’re familiar with it than I. I am.
170 00:16:30.270 ⇒ 00:16:31.730 Luke Daque: I used to
171 00:16:32.200 ⇒ 00:16:37.729 Luke Daque: work use with power bi before, but that was like, I don’t know 5, 6 years ago. So it’s.
172 00:16:37.730 ⇒ 00:16:38.179 Annie Yu: It’s great!
173 00:16:38.180 ⇒ 00:16:40.459 Luke Daque: Probably very different than what it is now.
174 00:16:41.190 ⇒ 00:16:44.480 Luke Daque: Okay, yeah, yeah.
175 00:16:45.040 ⇒ 00:16:55.879 Annie Yu: Yeah, I’ve only been more so like an end user with power. Bi, I did have access back then, but I didn’t really have how to do any editing.
176 00:16:59.450 ⇒ 00:17:00.350 Luke Daque: Yeah.
177 00:17:00.780 ⇒ 00:17:09.929 Luke Daque: But yeah, I used it was like an organizational account. And then an organizational to a a
178 00:17:10.099 ⇒ 00:17:19.349 Luke Daque: 3, 6 office, Microsoft, 3, 6, 5 account as well. And like we were using the desktop version and using windows as well. So yeah.
179 00:17:19.520 ⇒ 00:17:20.260 Annie Yu: Yeah.
180 00:17:27.908 ⇒ 00:17:42.420 Annie Yu: And the 1st solution ember that you typed set up virtual desktop. There’s also 2 types which, like one is cloud hosted, and one is local downloaded.
181 00:17:42.780 ⇒ 00:17:50.099 Annie Yu: and and I don’t. I don’t like I don’t wanna download a local one just because that slows down machine
182 00:17:52.990 ⇒ 00:17:55.890 Annie Yu: and like privacy concerns. All that.
183 00:17:59.850 ⇒ 00:18:01.123 Amber Lin: Sounds good.
184 00:18:06.040 ⇒ 00:18:07.000 Amber Lin: Great.
185 00:18:08.930 ⇒ 00:18:11.969 Amber Lin: Okay. I’ll book a meeting for us tomorrow we’ll meet. Then.
186 00:18:13.220 ⇒ 00:18:13.880 Luke Daque: Yeah, that’s good.
187 00:18:13.880 ⇒ 00:18:14.470 Amber Lin: Alrighty!
188 00:18:14.470 ⇒ 00:18:15.710 Annie Yu: November. Thank you.
189 00:18:15.710 ⇒ 00:18:16.540 Amber Lin: Bye.
190 00:18:16.540 ⇒ 00:18:17.579 Luke Daque: Thanks, bye, bye.